Name
Title | ||
|---|---|---|
Global exponential stability of almost periodic solution of delayed neural networks with discontinuous activations |
Abstract | ||
|---|---|---|
In this paper, we study the existence, uniqueness and stability of almost periodic solution for the class of delayed neural networks. The neural network considered in this paper employs the activation functions which are discontinuous monotone increasing and (possibly) unbounded. Under a new sufficient condition, we prove that the neural network has a unique almost periodic solution, which is globally exponentially stable. Moreover, the obtained conclusion is applied to prove the existence and stability of periodic solution (or equilibrium point) for delayed neural networks with periodic coefficients (or constant coefficients). We also give some illustrative numerical examples to show the effectiveness of our results. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.ins.2012.07.040 | Inf. Sci. |
Keywords | Field | DocType |
delayed neural network,periodic solution,equilibrium point,global exponential stability,neural network,constant coefficient,exponentially stable,activation function,new sufficient condition,periodic coefficient,discontinuous activation,illustrative numerical example | Uniqueness,Mathematical analysis,Constant coefficients,Equilibrium point,Exponential stability,Artificial neural network,Periodic graph (geometry),Mathematics,Monotone polygon | Journal |
Volume | ISSN | Citations |
220, | 0020-0255 | 41 |
PageRank | References | Authors |
1.33 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sitian Qin | 1 | 244 | 23.00 |
| Xiaoping Xue | 2 | 186 | 17.00 |
| Peng Wang | 3 | 41 | 1.33 |